{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n",
    "\n",
    "*No changes were made to the contents of this notebook from the original.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n",
    "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n",
    "We’ll start by setting up the notebook for plotting and importing the functions we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with ``plt.plot``\n",
    "\n",
    "In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\n",
    "It turns out that this same function can produce scatter plots as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ewhg9kGesAIbX0KMH8owVwPAagh7IM1YAw2sIeiDPWAEMryHogTxjBTC8hslYIM9YAQyv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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdaae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, 'o', color='black');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ijo5GSUkJLC0tER8fjyFDhrT7vsjISNjb2yM4OFgvQTvEcQAtzWVUHB2H4dCh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\nYmKosBNCiIn5/5UUNVEacdwFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf7e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
    "    plt.plot(rng.rand(5), rng.rand(5), marker,\n",
    "             label=\"marker='{0}'\".format(marker))\n",
    "plt.legend(numpoints=1)\n",
    "plt.xlim(0, 1.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YAwcOhJOTE3JycuDj48M6DhEYmUyGHj16oE+fPnBwcEDnzp2h0WhsYuTas54dUy/0x49W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\nTwghIkeFnhBCRI4KPSGEiNz/Aak6QmZ56l4lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1023b1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-ok');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YTCam1C0wtHLZ4XAwJXKBoaczMV83Id4XCQkJOHv2LFOd12QywWazYdasWRN6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\nLjyprHyiCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7a6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-p', color='gray',\n",
    "         markersize=15, linewidth=4,\n",
    "         markerfacecolor='white',\n",
    "         markeredgecolor='gray',\n",
    "         markeredgewidth=2)\n",
    "plt.ylim(-1.2, 1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\n",
    "For a full description of the options available, refer to the ``plt.plot`` documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with ``plt.scatter``\n",
    "\n",
    "A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8a18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, marker='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
    "\n",
    "Let's show this by creating a random scatter plot with points of many colors and sizes.\n",
    "In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BbCa7IqzNRpMroxfBVGbwSBij4aZYlvNlzs/PMTm3xAOnDuNyOxg42EVXX4vJ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8fce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "colors = rng.rand(100)\n",
    "sizes = 1000 * rng.rand(100)\n",
    "\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\n",
    "            cmap='viridis')\n",
    "plt.colorbar();  # show color scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\n",
    "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n",
    "\n",
    "For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xWSboCmOzWimUimQX80xcnOTQA/dt4Ce3eRaTSZwbmPrp9PmYWVjARgWn8+qT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1107c79b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "features = iris.data.T\n",
    "\n",
    "plt.scatter(features[0], features[1], alpha=0.2,\n",
    "            s=100*features[3], c=iris.target, cmap='viridis')\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n",
    "the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
    "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plot`` Versus ``scatter``: A Note on Efficiency\n",
    "\n",
    "Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\n",
    "The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
    "In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
    "For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
